===============================================================================
	Torch 3.1
===============================================================================
	
core
====
        * Bagging.cc
                - Initialize "is_selected_examples" to NULL.

        * ClassFormatDataSet.cc
                - Convert all the frames of targets, and not only the
                first one.

        * ClassMeasurer.cc
                - Scan all the frames of inputs to compute classification
                error, and not only the first one.
                - The confusion matrix computation option at the end of
		the training as been removed. The confusion matrix is still
		available at each iteration.

        * CmdOption.cc
                - When calling loadXFile(), a string not always allocated
                was freed.

        * DiskDataSet.cc
                - The number of examples was computed using io_inputs, even
                if it was NULL.

        * KFold.cc
                - The size of the folds is more balanced for small folds.

	* MemoryDataSet.cc
                - The number of examples was computed using io_inputs, even
                if it was NULL.

	* MemoryXFile.cc
	        - The EOF flag was not updated when reaching the end of file
		while reading with the scanf method.

        * MultiClassFormat.cc
                - Serious bug when auto-detecting classes corrected.

        * Random.cc
                - Using now memmove instead of memcopy (possibility of
		overlapping source and destination).

matrix
======
        * Mat.cc and Mat.h
                - Initializing a matrix from a "real *" now possible.

gradients
=========

        * SumMachine.cc
                - The gradient is now well back-propagated.

kernels
=======
        * QCTrainer.cc
                - Bug when updating alpha corrected. Note: very rare
                case in practice. Thanks to Stephen Schiller for the
                report of this tricky bug.

distributions
=============
       * DiagonalGMM.cc
                - log_probabilities are now correctely computed for the viterbi case
                - frameExpectation becomes frameDecision
                - keep the best gaussian for the current frame

       * Distribution.cc
                - resize the output of the machine correctely
                - added decision and frameDecision method

       * HMM.cc
                - added states shared parameters

       * MAPDiagonalGMM.cc
                - adapt the parameters only if the gaussian have "seen" at
                  least one frame

       * Multinomial.cc
                - added equal initialization option
                - added check that the log weights are number
                - frameExpectation becomes frameDecision

       * NLLCriterion.cc
                - correct the beta and outputs resize

       * ParzenDistribution.cc
                - correct the resize of the log_probablities
                - added frameExpectation method

       * TableLookupDistribution.cc
                - correction of some bugs to calculate the log probablity

       * ViterbiTrainer.cc ,ViterbiTrainer.h
                - this class is suppressed, it was already included in EMTrainer


datasets
========

      * IOHTK.cc
               - correction of some major bugs when reading the HTK files on
                 disk on unsequential mode
               - correction of major bug on double mode

      * IOHTKTarget.cc
               - space followed by tabulation allowed instead of space only
               - added some check

      * IOHTKTarget.h
               - make saveSequence static

      * Vocabulary.cc
               - bound check corrected for the number of words

speech
======

      * EditDistance.{cc,h} and EditDistanceMeasurer.{cc,h}
               - Add a constructor option to print a confusion matrix as well

      * WordSeg.{cc,h} and FrameSeg.{cc,h}
               - Two new class to keep the word and frame segmentations found
                 by viterbi decoding. These are kept separate so as to be
                 compatible with any new subclass of SpeechHMM.

      * WordSegMeasurer.{cc,h} and FrameSegMeasurer.{cc,h}
               - Measurers that outputs information related to the number
                 of errors in terms of word error rate (WordSegMeasurer) or
                 frame error rate (FrameSegMeasurer), using the EditDistance
                 class to compute the error.

      * SimpleDecoderSpeechHMM.{cc,h}
               - Modifications to take into account the new WordSeg and FrameSeg
                 classes
               - added a structure (previous_states[] and n_previous_states)
                 to speedup the decoding process by only looping on existing
                 transitions and not all possible transitions
               - Added an option to be able to decode using "forced alignment",
                 hence instead of decoding on the whole grammar, decoding on
                 a given (true) sentence

      * SpeechHMM.cc
               - bug corrections related to initialization